The Arabic Speech Corpus is a Modern Standard Arabic (MSA) speech corpus for speech synthesis. The corpus contains phonetic and orthographic transcriptions of more than 3.7 hours of MSA speech aligned with recorded speech on the phoneme level. The annotations include word stress marks on the individual phonemes. The Arabic Speech Corpus was built as part of a doctoral project by Nawar Halabi at the University of Southampton funded by MicroLinkPC who own an exclusive license to commercialise the corpus, but the corpus is available for strictly non-commercial purposes through the official Arabic Speech Corpus website. It is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. == Purpose == The corpus was mainly built for speech synthesis purposes, specifically Speech Synthesis, but the corpus has been used for building HMM based voices in Arabic. It was also used to automatically align other speech corpora with their phonetic transcript and could be used as part of a larger corpus for training speech recognition systems. == Contents == The package contains the following: 1813 .wav files containing spoken utterances. 1813 .lab files containing text utterances. 1813 .TextGrid files containing the phoneme labels with time stamps of the boundaries where these occur in the .wav files. phonetic-transcript.txt which has the form "[wav_filename]" "[Phoneme Sequence]" in every line. orthographic-transcript.txt which has the form "[wav_filename]" "[Orthographic Transcript]" in every line. Orthography is in Buckwalter Format which is friendlier where there is software that does not read Arabic script. It can be easily converted back to Arabic. There is an extra 18 minutes of fully annotated corpus (separate from above but with the same structure as above) which was used to evaluated the corpus (see PhD thesis). The corpus was also used to prove that using automatically extracted, orthography-based stress marks improve the quality of speech synthesis in MSA.
Cone tracing
Cone tracing and beam tracing are a derivative of the ray tracing algorithm that replaces rays, which have no thickness, with thick rays. == Principles == In ray tracing, rays are often modeled as geometric ray with no thickness to perform efficient geometric queries such as a ray-triangle intersection. From a physics of light transport point of view, however, this is an inaccurate model provided the pixel on the sensor plane has non-zero area. In the simplified pinhole camera optics model, the energy reaching the pixel comes from the integral of radiance from the solid angle by which the sensor pixel sees the scene through the pinhole at the focal plane. This yields the key notion of pixel footprint on surfaces or in the texture space, which is the back projection of the pixel on to the scene. Note that this approach can also represent a lens-based camera and thus depth of field effects, using a cone whose cross-section decreases from the lens size to zero at the focal plane, and then increases. Real optical system do not focus on exact points because of diffraction and imperfections. This can be modeled with a point spread function (PSF) weighted within a solid angle larger than the pixel. From a signal processing point of view, ignoring the point spread function and approximating the integral of radiance with a single, central sample (through a ray with no thickness) can lead to strong aliasing because the "projected geometric signal" has very high frequencies exceeding the Nyquist-Shannon maximal frequency that can be represented using the uniform pixel sampling rate. The physically based image formation model can be approximated by the convolution with the point spread function assuming the function is shift-invariant and linear. In practice, techniques such as multisample anti-aliasing estimate this cone-based model by oversampling the signal and then performing a convolution (the reconstruction filter). The backprojected cone footprint onto the scene can also be used to directly pre-filter the geometry and textures of the scene. Note that contrary to intuition, the reconstruction filter should not be the pixel footprint (as the pinhole camera model would suggest), since a box filter has poor spectral properties. Conversely, the ideal sinc function is not practical, having infinite support with possibly negative values which often creates ringing artifacts due to the Gibbs phenomenon. A Gaussian or a Lanczos filter are considered good compromises. == Computer graphics models == Cone and Beam early papers rely on different simplifications: the first considers a circular section and treats the intersection with various possible shapes. The second treats an accurate pyramidal beam through the pixel and along a complex path, but it only works for polyhedrical shapes. Cone tracing solves certain problems related to sampling and aliasing, which can plague conventional ray tracing. However, cone tracing creates a host of problems of its own. For example, just intersecting a cone with scene geometry leads to an enormous variety of possible results. For this reason, cone tracing has remained mostly unpopular. In recent years, increases in computer speed have made Monte Carlo algorithms like distributed ray tracing - i.e. stochastic explicit integration of the pixel - much more used than cone tracing because the results are exact provided enough samples are used. But the convergence is so slow that even in the context of off-line rendering a huge amount of time can be required to avoid noise. Differential cone-tracing, considering a differential angular neighborhood around a ray, avoids the complexity of exact geometry intersection but requires a LOD representation of the geometry and appearance of the objects. MIPmapping is an approximation of it limited to the integration of the surface texture within a cone footprint. Differential ray-tracing extends it to textured surfaces viewed through complex paths of cones reflected or refracted by curved surfaces. Raymarching methods over signed distance fields (SDFs) naturally allow easy use of cone-like tracing, at zero additional cost to the tracing, and both speeds up tracing and improves quality. Voxel cone tracing is a real-time algorithm that uses a hierarchical voxel representation of scene geometry, such as a sparse voxel octree, to support fast cone tracing for indirect illumination. This approach allows for the approximation of effects like glossy reflections and ambient occlusion at interactive framerates without the need for precomputation.
Play Integrity API
Play Integrity API (formerly known as SafetyNet) consists of several application programming interfaces (APIs) offered by the Google Play Services to support security sensitive applications and enforce DRM. Currently, these APIs include device integrity verification, app verification, recaptcha and web address verification. It uses an environment called DroidGuard to perform the attestation. == Attestation == The SafetyNet Attestation API, one of the APIs under the SafetyNet umbrella, provides verification that the integrity of the device is not compromised. In practice, non-official ROMs such as LineageOS fail the hardware attestation and thus prevent the user from using a non-compliant ROM with third-party apps (mainly banking) that require the API. Due to this, some consider this a monopolistic practice deterring the entrance of competing mobile operating systems in the market. It requires a network connection to Google servers and validates the hardware signatures. Amongst the checks, the API looks for bootloader unlock status, ROM signatures, kernel strings, it also uses AVB2.0 and dm-verity attestations. Upon successful checks, Google Play will mark the device as Certified. The attestation runs in an environment called DroidGuard (com.google.android.gms.unstable). The SafetyNet Attestation API (one of the four APIs under the SafetyNet umbrella) has been deprecated. As of 6 October 2023, Google planned to replace it with the Play Integrity API by the end of January 2025. The transition ended on 20 May 2025, breaking applications which hadn't been updated. These attestations are offered by Google Play Services and thus are not available on free Android environments, like AOSP. Therefore, developers can require the API to be available and may refuse to execute on AOSP builds. == Google Play Protect == Under the same umbrella, Play Protect is a mechanism to find and remove "vulnerable" apps from one's Android device as well as store apps. Although it's meant to scan for malware-containing apps, it also looks for non-DRM compliant apps. == Criticism == Multiple groups have criticised SafetyNet and the Play Integrity API. Criticisms include that it offers weaker protection compared to alternatives such as Android's hardware attestation API, which provides a stronger form of verification while having the ability to remain compatible with more secure Android operating systems like GrapheneOS. Critics argued it undermines competition by effectively requiring developers to rely on Google's proprietary services, strengthening its monopoly over the Android ecosystem and disadvantaging alternative, privacy-focused operating systems. Users have also developed tools, such as the Play Integrity Fix module for Magisk/KernelSU/APatch, which tricks the attestation using leaked fingerprints of vulnerable devices. Furthermore, some have questioned the effectiveness of the attestation, claiming it does not deliver the level of security promised by Google and instead serves more as a form of vendor lock-in than a meaningful security measure. Activists have also raised concerns that it may violate antitrust and competition laws, like the Digital Markets Act.
VieON
VieON is an mobile application for television and video on demand provided by VieON Joint Stock Company (formerly Dzones), a subsidiary of DatVietVAC Media and Entertainment Group in Vietnam. The app was launched in 2020, featuring over 140 domestic and international television channels, original series, popular entertainment programs known nationwide, top-tier sports events and live streaming of major events. Additionally, VieON provides animated films, television series and television programs from various countries such as South Korea and China. == History == The application was planned for development in 2016, with the cooperation of strategic consulting partner BCG Digital Ventures from the United States. Prior to 2020, VieON was a rebranded version of VTVcab ON, a product managed by Vietnam Cable Television Corporation (VTVCab) and DatVietVAC. On June 15, 2020, after four years of research and testing, the new version of VieON was officially released by DatVietVAC Group, with Vie Channel Joint Stock Company as the business entity and service provider. This is considered the official launch date of the application. On July 21, 2023, VieON transitioned its business operations and service provision to VieON Joint Stock Company. In January 2024, VieON officially launched its global version, VieON Global, targeting Vietnamese users living abroad. == Background == According to Kantar Media Vietnam, up to 84% of Vietnamese people aged 15–54 use social media daily, and in a similar survey by Nielsen, 90% of respondents said they watch live TV weekly. Additionally, according to research organization Muvi, Southeast Asia's OTT market revenue could reach $650 million annually starting next year. Understanding this, DatVietVAC Group has planned to research and develop an OTT application, even though the Vietnamese market already has some major players such as FPT Play and the international giant Netflix. Additionally, DatVietVAC does not hide its ambition to make this application the number one entertainment channel for Vietnamese people.
Plane Finder
Plane Finder is a United Kingdom-based real-time flight tracking service launched in 2009, that is able to show flight data globally. The data available includes flight numbers, how fast an aircraft is moving, its elevation and destination of travel. Several variants of the service are available as mobile apps including free, premium 3D and augmented reality versions. The flight tracking map and database can be accessed by web browsers. Plane Finder allows registered users to share their ADS-B and MLAT data via the Plane Finder ADS-B Client, available for macOS, Windows and Linux. Plane Finder supports VFR charts from NATS, and was the first major flight tracking app to introduce a replay feature, allowing users to replay flights dating back to 2011. == Flight tracking == Plane Finder collects data from its own global network of receivers, using the following sources. === Automatic dependent surveillance-broadcast (ADS-B) === A network of automatic dependent surveillance-broadcast (ADS-B) receivers gathers aircraft data such as callsign, position and speed. Plane Finder serves to supplement this data with additional information, including aircraft registration/tail number, departure airport, destination, artwork, and photographs. Plane Finder users can apply for an ADS-B receiver in exchange for their flight data. === Multilateration (MLAT) === To deliver aircraft position data where ADS-B is unavailable, Plane Finder uses multilateration (MLAT). Using three or more receivers running Plane Finder client software, monitoring the aircraft simultaneously, the aircraft’s position is calculated using receiver location and accurate timestamps. While European airspace is widely covered, only some parts of North American airspace are covered. === Federal Aviation Administration (FAA) feed === ADS-B is prevalent across Europe and Australia, but not in North America. Where MLAT or ADS-B data is unavailable, a feed from the Federal Aviation Administration provides flight information. The FAA feed covers United States and Canadian airspace, including bordering areas of the Atlantic and Pacific Oceans. === FLARM feed === Plane Finder collects data from a centralised FLARM feed, for monitoring small aircraft and gliders. == Flight data source == The Plane Finder website and database is widely used as an information source to support articles in the media. The Independent used Plane Finder flight tracking to demonstrate to readers the flight path of flight MT2706, which turned back as a result of last minute Egyptian government flight restrictions on 6 November 2015. The Independent also used Plane Finder information to demonstrate a timeline of the speed/altitude of flight 7K 9268, a Russian plane which crashed on 31 October 2015. The BBC cited Plane Finder in regard to the point at which at British Airways flight turned back to Heathrow Airport to make an emergency landing after smoke was seen coming from its engines. Plane Finder data has also been used to create original imagery for the media, such as the Washington Post, which used Plane Finder as a source to show flight patterns immediately after the Brussels bombings in March 2016.
Weight initialization
In deep learning, weight initialization or parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training: weight initialization is the pre-training step of assigning initial values to these parameters. The choice of weight initialization method affects the speed of convergence, the scale of neural activation within the network, the scale of gradient signals during backpropagation, and the quality of the final model. Proper initialization is necessary for avoiding issues such as vanishing and exploding gradients and activation function saturation. Note that even though this article is titled "weight initialization", both weights and biases are used in a neural network as trainable parameters, so this article describes how both of these are initialized. Similarly, trainable parameters in convolutional neural networks (CNNs) are called kernels and biases, and this article also describes these. == Constant initialization == We discuss the main methods of initialization in the context of a multilayer perceptron (MLP). Specific strategies for initializing other network architectures are discussed in later sections. For an MLP, there are only two kinds of trainable parameters, called weights and biases. Each layer l {\displaystyle l} contains a weight matrix W ( l ) ∈ R n l − 1 × n l {\displaystyle W^{(l)}\in \mathbb {R} ^{n_{l-1}\times n_{l}}} and a bias vector b ( l ) ∈ R n l {\displaystyle b^{(l)}\in \mathbb {R} ^{n_{l}}} , where n l {\displaystyle n_{l}} is the number of neurons in that layer. A weight initialization method is an algorithm for setting the initial values for W ( l ) , b ( l ) {\displaystyle W^{(l)},b^{(l)}} for each layer l {\displaystyle l} . The simplest form is zero initialization: W ( l ) = 0 , b ( l ) = 0 {\displaystyle W^{(l)}=0,b^{(l)}=0} Zero initialization is usually used for initializing biases, but it is not used for initializing weights, as it leads to symmetry in the network, causing all neurons to learn the same features. In this page, we assume b = 0 {\displaystyle b=0} unless otherwise stated. Recurrent neural networks typically use activation functions with bounded range, such as sigmoid and tanh, since unbounded activation may cause exploding values. (Le, Jaitly, Hinton, 2015) suggested initializing weights in the recurrent parts of the network to identity and zero bias, similar to the idea of residual connections and LSTM with no forget gate. In most cases, the biases are initialized to zero, though some situations can use a nonzero initialization. For example, in multiplicative units, such as the forget gate of LSTM, the bias can be initialized to 1 to allow good gradient signal through the gate. For neurons with ReLU activation, one can initialize the bias to a small positive value like 0.1, so that the gradient is likely nonzero at initialization, avoiding the dying ReLU problem. == Random initialization == Random initialization means sampling the weights from a normal distribution or a uniform distribution, usually independently. === LeCun initialization === LeCun initialization, popularized in (LeCun et al., 1998), is designed to preserve the variance of neural activations during the forward pass. It samples each entry in W ( l ) {\displaystyle W^{(l)}} independently from a distribution with mean 0 and variance 1 / n l − 1 {\displaystyle 1/n_{l-1}} . For example, if the distribution is a continuous uniform distribution, then the distribution is U ( ± 3 / n l − 1 ) {\displaystyle {\mathcal {U}}(\pm {\sqrt {3/n_{l-1}}})} . === Glorot initialization === Glorot initialization (or Xavier initialization) was proposed by Xavier Glorot and Yoshua Bengio. It was designed as a compromise between two goals: to preserve activation variance during the forward pass and to preserve gradient variance during the backward pass. For uniform initialization, it samples each entry in W ( l ) {\displaystyle W^{(l)}} independently and identically from U ( ± 6 / ( n l + 1 + n l − 1 ) ) {\displaystyle {\mathcal {U}}(\pm {\sqrt {6/(n_{l+1}+n_{l-1})}})} . In the context, n l − 1 {\displaystyle n_{l-1}} is also called the "fan-in", and n l + 1 {\displaystyle n_{l+1}} the "fan-out". When the fan-in and fan-out are equal, then Glorot initialization is the same as LeCun initialization. === He initialization === As Glorot initialization performs poorly for ReLU activation, He initialization (or Kaiming initialization) was proposed by Kaiming He et al. for networks with ReLU activation. It samples each entry in W ( l ) {\displaystyle W^{(l)}} from N ( 0 , 2 / n l − 1 ) {\displaystyle {\mathcal {N}}(0,2/n_{l-1})} . === Orthogonal initialization === (Saxe et al. 2013) proposed orthogonal initialization: initializing weight matrices as uniformly random (according to the Haar measure) semi-orthogonal matrices, multiplied by a factor that depends on the activation function of the layer. It was designed so that if one initializes a deep linear network this way, then its training time until convergence is independent of depth. Sampling a uniformly random semi-orthogonal matrix can be done by initializing X {\displaystyle X} by IID sampling its entries from a standard normal distribution, then calculate ( X X ⊤ ) − 1 / 2 X {\displaystyle \left(XX^{\top }\right)^{-1/2}X} or its transpose, depending on whether X {\displaystyle X} is tall or wide. For CNN kernels with odd widths and heights, orthogonal initialization is done this way: initialize the central point by a semi-orthogonal matrix, and fill the other entries with zero. As an illustration, a kernel K {\displaystyle K} of shape 3 × 3 × c × c ′ {\displaystyle 3\times 3\times c\times c'} is initialized by filling K [ 2 , 2 , : , : ] {\displaystyle K[2,2,:,:]} with the entries of a random semi-orthogonal matrix of shape c × c ′ {\displaystyle c\times c'} , and the other entries with zero. (Balduzzi et al., 2017) used it with stride 1 and zero-padding. This is sometimes called the Orthogonal Delta initialization. Related to this approach, unitary initialization proposes to parameterize the weight matrices to be unitary matrices, with the result that at initialization they are random unitary matrices (and throughout training, they remain unitary). This is found to improve long-sequence modelling in LSTM. Orthogonal initialization has been generalized to layer-sequential unit-variance (LSUV) initialization. It is a data-dependent initialization method, and can be used in convolutional neural networks. It first initializes weights of each convolution or fully connected layer with orthonormal matrices. Then, proceeding from the first to the last layer, it runs a forward pass on a random minibatch, and divides the layer's weights by the standard deviation of its output, so that its output has variance approximately 1. === Fixup initialization === In 2015, the introduction of residual connections allowed very deep neural networks to be trained, much deeper than the ~20 layers of the previous state of the art (such as the VGG-19). Residual connections gave rise to their own weight initialization problems and strategies. These are sometimes called "normalization-free" methods, since using residual connection could stabilize the training of a deep neural network so much that normalizations become unnecessary. Fixup initialization is designed specifically for networks with residual connections and without batch normalization, as follows: Initialize the classification layer and the last layer of each residual branch to 0. Initialize every other layer using a standard method (such as He initialization), and scale only the weight layers inside residual branches by L − 1 2 m − 2 {\displaystyle L^{-{\frac {1}{2m-2}}}} . Add a scalar multiplier (initialized at 1) in every branch and a scalar bias (initialized at 0) before each convolution, linear, and element-wise activation layer. Similarly, T-Fixup initialization is designed for Transformers without layer normalization. === Others === Instead of initializing all weights with random values on the order of O ( 1 / n ) {\displaystyle O(1/{\sqrt {n}})} , sparse initialization initialized only a small subset of the weights with larger random values, and the other weights zero, so that the total variance is still on the order of O ( 1 ) {\displaystyle O(1)} . Random walk initialization was designed for MLP so that during backpropagation, the L2 norm of gradient at each layer performs an unbiased random walk as one moves from the last layer to the first. Looks linear initialization was designed to allow the neural network to behave like a deep linear network at initialization, since W R e L U ( x ) − W R e L U ( − x ) = W x {\displaystyle W\;\mathrm {ReLU} (x)-W\;\mathrm {ReLU} (-x)=Wx} . It initializes a matrix W {\displaystyle W} of shape R n 2 × m {\displaystyle \mathbb {R} ^{{\frac {n}{2}}\times m}} by any method, such as orthogonal initialization, t
Termcap
Termcap (terminal capability) is a legacy software library and database used on Unix-like computers that enables programs to use display computer terminals in a terminal-independent manner, which greatly simplifies the process of writing portable text mode applications. It was superseded by the terminfo database used by ncurses, tput, and other programs. A termcap database can describe the capabilities of hundreds of different display terminals. This allows programs to have character-based display output, independent of the type of terminal. On-screen text editors such as vi and Emacs are examples of programs that may use termcap. Other programs are listed in the Termcap category. Access to the termcap database was usually provided by separate libraries, e.g. GNU Termcap. Examples of what the database describes: how many columns wide the display is what string to send to move the cursor to an arbitrary position (including how to encode the row and column numbers) how to scroll the screen up one or several lines how much padding is needed for such a scrolling operation. == History == Bill Joy wrote the first termcap library in 1978 for the Berkeley Unix operating system; it has since been ported to most Unix and Unix-like environments, even OS-9. Joy's design was reportedly influenced by the design of the terminal data store in the earlier Incompatible Timesharing System. == Data model == Termcap databases consist of one or more descriptions of terminals. === Indices === Each description must contain the canonical name of the terminal. It may also contain one or more aliases for the name of the terminal. The canonical name or aliases are the keys by which the library searches the termcap database. === Data values === The description contains one or more capabilities, which have conventional names. The capabilities are typed: boolean, numeric and string. The termcap library has no predetermined type for each capability name. It determines the types of each capability by the syntax: string capabilities have an "=" between the capability name and its value, numeric capabilities have a "#" between the capability name and its value, and boolean capabilities have no associated value (they are always true if specified). Applications which use termcap do expect specific types for the commonly used capabilities, and obtain the values of capabilities from the termcap database using library calls that return successfully only when the database contents matches the assumed type. === Hierarchy === Termcap descriptions can be constructed by including the contents of one description in another, suppressing capabilities from the included description or overriding or adding capabilities. No matter what storage model is used, the termcap library constructs the terminal description from the requested description, including, suppressing or overriding at the time of the request. == Storage model == Termcap data is stored as text, making it simple to modify. The text can be retrieved by the termcap library from files or environment variables. === Environment variables === The TERM environment variable contains the terminal type name. The TERMCAP environment variable may contain a termcap database. It is most often used to store a single termcap description, set by a terminal emulator to provide the terminal's characteristics to the shell and dependent programs. The TERMPATH environment variable is supported by newer termcap implementations and defines a search path for termcap files. === Flat file === The original (and most common) implementation of the termcap library retrieves data from a flat text file. Searching a large termcap file, e.g., 500 kB, can be slow. To aid performance, a utility such as reorder is used to put the most frequently used entries near the beginning of the file. === Hashed database === 4.4BSD based implementations of termcap store the terminal description in a hashed database (e.g., something like Berkeley DB version 1.85). These store two types of records: aliases which point to the canonical entry, and the canonical entry itself. The text of the termcap entry is stored literally. == Limitations and extensions == The original termcap implementation was designed to use little memory: the first name is two characters, to fit in 16 bits capability names are two characters descriptions are limited to 1023 characters. only one termcap entry with its definitions can be included, and must be at the end. Newer implementations of the termcap interface generally do not require the two-character name at the beginning of the entry. Capability names are still two characters in all implementations. The tgetent function used to read the terminal description uses a buffer whose size must be large enough for the data, and is assumed to be 1024 characters. Newer implementations of the termcap interface may relax this constraint by allowing a null pointer in place of the fixed buffer, or by hiding the data which would not fit, e.g., via the ZZ capability in NetBSD termcap. The terminfo library interface also emulates the termcap interface, and does not actually use the fixed-size buffer. The terminfo library's emulation of termcap allows multiple other entries to be included without restricting the position. A few other newer implementations of the termcap library may also provide this ability, though it is not well documented. == Obsolete features == A special capability, the "hz" capability, was defined specifically to support the Hazeltine 1500 terminal, which had the unfortunate characteristic of using the ASCII tilde character ('~') as a control sequence introducer. In order to support that terminal, not only did code that used the database have to know about using the tilde to introduce certain control sequences, but it also had to know to substitute another printable character for any tildes in the displayed text, since a tilde in the text would be interpreted by the terminal as the start of a control sequence, resulting in missing text and screen garbling. Additionally, attribute markers (such as start and end of underlining) themselves took up space on the screen. Comments in the database source code often referred to this as "Hazeltine braindamage". Since the Hazeltine 1500 was a widely used terminal in the late 1970s, it was important for applications to be able to deal with its limitations.